Why Computational Engineering?

Computational modeling and simulation is a primary means of analysis in national laboratories and industry. As engineering systems have become more advanced, emphasis is placed on carrying out simulation-based design at scale of these systems, with increased focus on nonlinear, multi-physics, and multi-scale analyses. Realistic applications in computational science and engineering involve multi-fidelity data that need to be assimilated in the simulations for validation and robust predictions. The new paradigm of physics-informed machine learning has emerged as a powerful simulation method that seamlessly integrates simulations and data. 

Why Brown?

Brown University has nationally recognized and highly ranked programs in engineering and applied mathematics. Many of our faculty are working on developing state-of-the-art numerical methods and machine learning approaches, with applications that are of particular relevance to this program. The School of Engineering at Brown University has also played a central role in the development of finite element methods and software. ABAQUS software, arguably the most widely adopted and used commercial finite element software package in the world, was created by two Brown University School of Engineering Ph.D. graduates.

Who is the Program Designed For?

Students with recent bachelor of science degrees in engineering, applied mathematics, computer science, and other related disciplines pursuing careers that involve advanced modeling and simulation in engineering and physical sciences are encouraged to apply. Working professionals whose success on the job depends on their ability to competently perform high-fidelity engineering simulations with data assimilation and machine learning expertise are also encouraged to apply.

Program Outcomes

Upon completion of the program coursework, students will:

• Gain the understanding of a significant role that advanced data-driven simulation plays in industry and national laboratories
• Develop an appreciation for the power of multi-fidelity, data-driven modeling and simulation in contemporary engineering design
• Gain technical knowledge of the foundational subjects in computational engineering, including nonlinear finite element analysis and the integration of machine learning and data science
• Develop the necessary technical skills in data science and expertise to knowledgeably carry out practical engineering-scale, data-driven inference and simulations